DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-9, 11, and 13-20 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by US 2019/0291008 Cox et al. (hereinafter Cox).
Regarding claims 1, 13, and 17, Cox teaches a computer-implemented method, a non-transitory computer-readable memory, and a system configured to:
detect cheating in a virtual experience, comprising:
obtaining game information comprising game state information (Paragraph 0026 discloses capturing data representative of gameplay actions at various time intervals); and
generating, by a trained machine learning (ML) model and based on the game information, output that characterizes user behaviors in the virtual experience, wherein the output includes cheating analysis data that characterizes the user behaviors by indicating a cheating likelihood for one or more users (Paragraph 0029 discloses using the data representing gameplay actions as the input to a trained neural network model to determine a likelihood for the use of cheat software during a gameplay action).
Regarding claim 2, Cox teaches: wherein the trained ML model is trained using supervised learning using training data pre-generated during cheating scenarios (Paragraph 0018 discloses that the training set for the model includes past gameplay actions that have been labeled as cheating).
Regarding claim 4, Cox teaches: wherein the cheating analysis data is associated with a confidence score (Paragraph 0029 discloses a single floating-point number between 0 and 1 that represents an approximate probability of a cheating action).
Regarding claims 5, 16, and 20 Cox teaches: wherein the trained ML model is a single ML model that generates a plurality of labels, each corresponding to a particular type of cheating (Paragraph 0029 discloses output of the model as a multi-class classification indicating one of multiple types of unauthorized gameplay actions, and Fig. 2b shows a single model wherein there are two output labels).
Regarding claims 6, 14, and 18 Cox teaches: preprocessing the game information prior to the generating by flattening the game information and serializing the game information into a vector of numbers (Paragraph 0028 discloses encoding user gameplay information data into a vector. Label 240, Fig. 2A further shows encoded gameplay information as serialized data).
Regarding claim 7, Cox teaches: wherein the game information comprises time data and user avatar physics data, and indicates cheating when the user avatar physics data comprises values that violate one or more physics rules of the virtual experience (Paragraph 0027 discloses some parameters for candidate cheating actions, for example, a in change in yaw of a weapon during a shot; user distance to virtual objects; and user movement).
Regarding claim 8, Cox teaches: the cheating analysis data comprising a plurality of likelihoods, each likelihood corresponding to a particular type of cheating (Paragraph 0029 discloses multi-class classification indicating one of multiple types of unauthorized gameplay actions, for example, aim assistance, tracking of non-visible targets, or unauthorized movements to reach firing locations).
Regarding claim 9, Cox teaches: the cheating analysis data comprising a cheating likelihood label for each time window of a predetermined length (Labels 230a and 230b, Fig. 2A show the capture of gameplay actions during multiple time windows in the timeline of gameplay actions shown in Fig. 2A, Label 210, and Paragraphs 0028-0029 disclose using the data representing a single gameplay action or a set of actions as input to a trained model which outputs the likelihood of whether cheating software was used).
Regarding claims 11, 15, and 19, Cox teaches: in response to the cheating analysis data indicating with a likelihood exceeding a threshold probability that a user is cheating, performing an anti-cheat operation (Paragraph 0051 discloses that if the likelihood of performing an unauthorized game action exceeds an upper threshold, such as 90%, to directly impose penalties).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Cox.
Regarding claim 10, Cox lacks an explicit teaching of associating a threshold number of consecutive cheat labels within a time-window to the cheating-analysis data. Regardless, Cox discloses including multiple gameplay actions during a sliding window in a set of data to be provided to the output model to determine the likelihood of cheating (Paragraphs 0028-0029) and assessing multiple gameplay actions grouped together during a period of time (Paragraph 0014). Cox additionally discloses one or more upper threshold levels of tracked accumulation of unauthorized gameplay action may cause one or more penalties to be assessed (Paragraph 11). Cox also discloses using information about the user, such as an amount of time since a previous penalty (Paragraph 0021). Because the likelihood of a cheating is correlated to a given number of abnormal incidents in a given time period (i.e., a threshold amount in a given time window), it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Cox’s method by adding a variable representing a count to track how many consecutive actions have been labeled in a given time period as cheating actions by the model.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Cox, in view of US 2024/0001246 Soryal (hereinafter Soryal).
Regarding claim 3, Cox teaches the method of claim 1 but lacks wherein the trained ML models is game-specific or genre-specific. Regardless, Cox discloses that other types of model architectures, other than the neural network as disclosed, may be used in his method (Paragraph 0029).
Soryal teaches a trained ML model for detecting cheating in games wherein the ML model is game-specific (Paragraph 0017 discloses game specific ML models). Soryal teaches that the use of game-specific models allows for models that are tuned to the characteristics of a virtual environment (Paragraph 0049).
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified Cox’s method by replacing the neural network model with a game-specific model as disclosed by Soryal in order to tune the output of the model to a specific virtual environment.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Cox, in view of US 2025/0342182 Nabel (hereinafter Nabel).
Regarding claim 12, Cox teaches the method of claim 1 but lacks:
further comprising providing the cheating analysis data and game scripts to a language model for program synthesis to generate scripts robust to a type of cheating associated with the cheating analysis data.
However, Nabel teaches a system designed to send player data to a language model for program synthesis to generate scripts robust to a type of cheating associated with the player data. (Fig. 3 shows a system in which a user query, label 212, is processed to generate a LLM response, label 270, and Paragraph 0046 discloses an embodiment in which the user query is a prompt made with the intent to cheat in the game and the large language model generates an appropriate response).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Cox’s method by sending the cheating analysis data to a large language model to generate scripts robust to the type of cheating. One would be motivated to do this because doing so would turn the computer output of the model into something that can be interacted with by a human game player or moderator.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON LEE GARLAND whose telephone number is 571-272-0810. The examiner can normally be reached Monday - Friday, 8:30 - 5.
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/J.L.G./Examiner, Art Unit 3715
/WILLIAM H MCCULLOCH JR/Primary Examiner, Art Unit 3715
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